Use of prior knowledge to inform restoration projects in estuaries of GOM

July 28, 2017

# randomize author order
aut <- c('Marcus Beck', 'Kirsten Dorans', 'Jessica Renee Henkel', 'Kathryn Ireland', 'Ed Sherwood', 'Patricia Varela') %>% 
  sample %>% 
  paste(collapse = ', ')

cat('By', aut)

By Patricia Varela, Kathryn Ireland, Kirsten Dorans, Jessica Renee Henkel, Marcus Beck, Ed Sherwood

Deepwater Horizon Settlement Agreement

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Over $10B in Potential Restoration Activities

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Cumulative Effects of Restoration Activities?

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  • Vision to make it portable
  • Why Bayesian networks

Benefits

  • A general and flexible framework that can be applied to unique locations and is not limited by data availability
  • Explicit quantification of uncertainty and model updates with new data
  • More focused restoration towards specific regional issues
  • Improved ability to predict outcomes of proposed restoration projects

Tampa Bay was gross

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Tampa Bay is not as gross

Tampa Bay is not as gross

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But how much less gross??

But how much less gross??

Ed

  • Tampa Bay Background

Tampa Bay Data Sources

  • Rich WQ Monitoring Datatset (1974-)
    • Chlorophyll, salinity, dissolved oxygen, etc.
    • Depth-integrated
    • QAQC
  • Time series, monthly step - ~500 obs. per site

Tampa Bay Restoration Sites

  • Restoration sites in Tampa Bay, watershed
    • Habitat Establishment
    • Habitat Enhancement
    • Habitat Protection
    • Stormwater Controls
    • Point Source Controls
  • 571 projects, 1971 - 2016

Overall Workflow

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Developing Restoration Dataset

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Data plyring

  • Can we identify a change in water quality from restoration?
  • What data do we have?
  • Can we plyr the data to identify a signal?
  • Can we plyr the data as input to a BN?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?
  • Consider distance of sites from water quality stations?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?
  • Consider distance of sites from water quality stations?
  • Consider a cumulative effect?

Data plyring

WQ and restoration sites

  • Can we plyr the data to identify a signal?
  • How can continuous water quality be linked to discrete restoration activites?
  • Consider an effect of restoration site type?
  • Consider distance of sites from water quality stations?
  • Consider a cumulative effect?

Data plyring

WQ and restoration sites: Spatial match

Data plyring

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match

Data plyring

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after

Data plyring

WQ and restoration sites: Spatial match

WQ and restoration sites: Temporal match, before/after, slice

Data plyring

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 3
# Groups:   stat [1]
   stat     cmb     cval
  <int>   <chr>    <dbl>
1     7 hab_aft 8.255185
2     7 hab_bef 8.350187
3     7 wtr_aft 8.053273
4     7 wtr_bef 8.129733

Data plyring

What do the data look like? For one water quality station matched to many restoration sites…

WQ and restoration sites: Temporal match, before/after, slice

# A tibble: 4 x 4
   stat     hab     wtr     cval
  <int>  <fctr>  <fctr>    <dbl>
1     7 hab_aft wtr_aft 8.154229
2     7 hab_aft wtr_bef 8.192459
3     7 hab_bef wtr_aft 8.201730
4     7 hab_bef wtr_bef 8.239960

Data plyring

What do the data look like? For many water quality station matched to many restoration sites…

# A tibble: 20 x 4
    stat     hab     wtr      cval
   <int>  <fctr>  <fctr>     <dbl>
 1     6 hab_aft wtr_aft  8.903273
 2     6 hab_aft wtr_bef 11.720206
 3     6 hab_bef wtr_aft 11.902951
 4     6 hab_bef wtr_bef 14.719883
 5     7 hab_aft wtr_aft  8.154229
 6     7 hab_aft wtr_bef  8.192459
 7     7 hab_bef wtr_aft  8.201730
 8     7 hab_bef wtr_bef  8.239960
 9     8 hab_aft wtr_aft 19.867100
10     8 hab_aft wtr_bef 17.444274
11     8 hab_bef wtr_aft 17.331973
12     8 hab_bef wtr_bef 14.909147
13     9 hab_aft wtr_aft  9.030021
14     9 hab_aft wtr_bef  8.621069
15     9 hab_bef wtr_aft  8.398558
16     9 hab_bef wtr_bef  7.989606
17    11 hab_aft wtr_aft  6.576058
18    11 hab_aft wtr_bef  6.727664
19    11 hab_bef wtr_aft  8.112902
20    11 hab_bef wtr_bef  8.264508

Data plyring

What do the data look like? For many water quality station matched to many restoration sites…

Bayesian Network

Patricia

  • Specifics of BN
  • Outcomes/interpretation/applications

Conclusion

  • Next steps (all)